Participatory selection and characterization of quality protein maize (QPM) varieties in Savanna agro- ecological region of DR-Congo
Bibliographic record
Abstract
Maize (Zea mays L.) is a major cereal crop for human nutrition in the Democratic Republic of Congo (DR- Congo). Prevailing normal maize is deficient in two essential amino acids, lysine and tryptophan. Participatory variety selection was applied to select diversified quality protein maize (QPM) varieties that possess farmers’ preferred plant and grain traits. The varieties were planted with and without chemical fertilization. Selection was based primarily on agronomic traits such as time to maturity, plant and ear aspect, disease and insect resistance, yield and yield components as well as flour quality. There were significant differences among QPM varieties for several agronomic traits. The use of participatory approach in agricultural research allowed selection of one QPM, (QPMSRSYNTH), and one normal improved maize (AK9331-DMR-ESR-Y) for their yield advantage over currently released normal maize varieties in more than one criterion. The adoption of these newly introduced varieties is expected to be high since they were selected based on farmer’s preference. Key words: Quality protein maize, participatory varietal selection, DR-Congo.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".